Introduction

MarcoPolo securely connects AI to the systems you use to do your work. It provides a persistent workspace where AI can query, correlate, analyze, and act on the data across these systems. The data tools, the access firewall, and the context available in the workspace enable your AI to automate more of your work without compromising your data security - so that you don't have to worry about context switching across systems, manually cutting and pasting data, or writing boilerplate SQL or Python.

Any AI assistant - Claude, ChatGPT, Cursor, Codex, or custom agents - can connect to MarcoPolo via MCP. For Claude and Codex users, the Claude Plugin and Codex Plugin are the most effective ways to connect, providing skills and client-specific workflows that guide the assistant through proven data workflows.

How it Works

1. Connect: Link your databases (PostgreSQL, Snowflake, etc.), cloud storage (S3, OneDrive, etc.) and SaaS apps (Salesforce, Jira, etc.) to MarcoPolo once. Or install a demo connection and skip the credentials step.
2. Execute: When you ask a question, your AI writes a query file inside the workspace and runs it through the in-pod connection CLI.
3. Cache: The full result is materialized into a persistent DuckDB instance in your workspace, ready for follow-up analysis.
4. Refine: Only summaries and previews are returned to the AI's context window, keeping the conversation focused on reasoning rather than data transport.

Example: Cross-System Churn Analysis

Show me which customers churned last quarter and pull their support tickets.

AI runs workspace_shell("connection list --json")
   → Found: salesforce, jira, snowflake

AI runs workspace_shell("connection describe salesforce --json")
   → Discovered Account, Opportunity, Contact objects

AI writes connections/salesforce/queries/churned_accounts.sql
AI runs workspace_shell("connection query salesforce --file connections/salesforce/queries/churned_accounts.sql --json")
   → 18 churned accounts → DuckDB relation: salesforce_churned_accounts

AI writes connections/jira/queries/churned_tickets.sql
AI runs workspace_shell("connection query jira --file connections/jira/queries/churned_tickets.sql --json")
   → 94 tickets for those accounts → DuckDB relation: jira_churned_tickets

AI writes connections/DUCKDB/queries/churn_analysis.sql  (joins the two relations)
AI runs workspace_shell("connection query DUCKDB --file connections/DUCKDB/queries/churn_analysis.sql --json")
   → 65% of churned accounts had >2 unresolved escalations
   → 3 active accounts currently match this pattern

Summary returned to AI: <1KB. Full results cached in workspace: 4.2MB.

Two connections queried, joined through DuckDB, and analyzed — in one conversation.

Built for cross-system data work

• RevOps: Correlate Salesforce records with warehouse tables to generate pipeline metrics.
• Data Engineering: Profile datasets and automate query runs against Snowflake or BigQuery without leaving the chat.
• Infrastructure: Trace production issues across S3 logs and relational databases in a single conversation.
• Manufacturing: Run yield analysis and failure mode paretos across distributed data stores like Microsoft Fabric.
• Customer Success: Cross-reference support tickets with CRM data to identify account health patterns before renewal cycles.